Framework for Machine Learning-Based Pavement Marking Inspection and Geohash-Based Monitoring
Yong‐Han Kim, Kwonsik Song, Kyubyung Kang
Abstract
Pavement markings play a critical role in reducing crashes and improving safety on public roads. Although inspecting pavement markings at the right moment is essential, it is challenging due to the lack of resources for inspections and the geographical distribution of pavement markings. This study proposed a framework that (1) detects pavement markings using deep learning application, (2) effectively stores the information of the detected pavement markings to monitor and manage continuously, and (3) analyzes the condition of the detected pavement markings. The framework adopted the You Only Look Once (YOLO)-v3 algorithm to recognize ten different types of pavement markings. Once the pavement markings are identified, the framework converts GPS coordinates of the recognized pavement markings into Geohash values. Geohash is a multi-dimensional spatial location indexing algorithm that divides regions of the earth. Finally, the framework analyzes the visibility condition of the recognized pavement markings by computing contrast values between pavement markings and surrounding pavements. The framework was tested using selected pavement marking samples in Indiana. The results show that all pavement markings were correctly recognized with precise Geohash IDs and visibility conditions in a reasonable margin of errors. The framework is expected to reduce time and efforts to inspect and monitor various pavement markings.